A Deep Learning-based Model for Detecting Abnormalities on Brain MR Images for Triaging: Preliminary Results from a...

Area of Science:

• Medical Imaging
• Artificial Intelligence in Medicine
• Neurology

Background:

• Brain MR imaging is crucial for diagnosing neurological conditions.
• Accurate interpretation of MR images is vital for patient care.
• Developing automated tools can aid radiologists in detecting abnormalities.

Purpose of the Study:

• To develop a deep learning model for detecting brain abnormalities on MR images.
• To classify brain MRI findings as 'likely normal' or 'likely abnormal' using T2-weighted fluid-attenuated inversion recovery images.
• To evaluate the model's performance on heterogeneous datasets from different institutions.

Main Methods:

• A retrospective study utilizing a convolutional neural network (CNN) model.
• Training on large, multi-continental datasets (Datasets A and B) covering various pathologies.
• Testing models on independent datasets (subsets of A, B, and Dataset C) and comparing with radiology report annotations.

Main Results:

• Model A, trained on one institution's data and tested on another's (Dataset C), achieved an F1 score of 0.72.
• The same model (Model A) obtained an area under the receiver operating characteristic curve of 0.78 when compared to radiology reports.
• Performance was evaluated against both image-based annotations and radiology report labels.

Conclusions:

• The developed deep learning model demonstrates good performance in distinguishing between normal and abnormal brain MRIs.
• The model's ability to generalize across different institutions highlights its potential clinical utility.
• This AI approach shows promise for supporting the interpretation of brain MR imaging studies.